Prefetching is an effective technique for improving file access performance, which can significantly reduce access latency for I/O systems. In distributed storage systems, prefetching for metadata files is critical for the overall system performance. In this paper, an affinity-based metadata prefetching (AMP) scheme is proposed for metadata servers in large-scale distributed storage systems to provide aggressive metadata prefetching. Through mining useful information about metadata accesses from past history, AMP can discover metadata file affinities accurately and intelligently for prefetching. Compared with LRU and some of the latest file prefetching algorithms such as Nexus and C-Miner, our trace-driven simulations show that AMP can improve buffer cache hit rates by up to 12%, 4.5% and 4% respectively, while reduce the average response time by up to 60%, 12% and 8%, respectively.